Claude-skill-registry-data bio-epitranscriptomics-m6anet-analysis
Detect m6A modifications from Oxford Nanopore direct RNA sequencing using m6Anet. Use when analyzing epitranscriptomic modifications from long-read RNA data without immunoprecipitation.
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry-data
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry-data "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/m6anet-analysis" ~/.claude/skills/majiayu000-claude-skill-registry-data-bio-epitranscriptomics-m6anet-analysis && rm -rf "$T"
manifest:
data/m6anet-analysis/SKILL.mdsource content
m6Anet Analysis
Documentation: https://m6anet.readthedocs.io/
Data Preparation
# Basecall with Guppy (requires FAST5 files) guppy_basecaller \ -i fast5_dir \ -s basecalled \ --flowcell FLO-MIN106 \ --kit SQK-RNA002 # Align to transcriptome minimap2 -ax map-ont -uf transcriptome.fa reads.fastq > aligned.sam
Run m6Anet
from m6anet.utils import preprocess from m6anet import run_inference # Preprocess: extract features from FAST5 preprocess.run( fast5_dir='fast5_pass', out_dir='m6anet_data', reference='transcriptome.fa', n_processes=8 ) # Run m6A inference run_inference.run( input_dir='m6anet_data', out_dir='m6anet_results', n_processes=4 )
CLI Workflow
# Preprocess m6anet dataprep \ --input_dir fast5_pass \ --output_dir m6anet_data \ --reference transcriptome.fa \ --n_processes 8 # Inference m6anet inference \ --input_dir m6anet_data \ --output_dir m6anet_results \ --n_processes 4
Interpret Results
import pandas as pd results = pd.read_csv('m6anet_results/data.site_proba.csv') # Filter high-confidence m6A sites # probability > 0.9: High confidence threshold m6a_sites = results[results['probability_modified'] > 0.9]
Related Skills
- long-read-sequencing - ONT data processing
- m6a-peak-calling - MeRIP-seq alternative
- modification-visualization - Plot m6A sites